},
                },
            }
        }
    }
def _get_kwargs_value(kwargs: Any, key: str, default_value: Any) -> Any:
    """Get the value of the key if present. Else get the default_value."""
    if key in kwargs:
        return kwargs.get(key)
    return default_value
[docs]class OpenSearchVectorSearch(VectorStore):
    """Wrapper around OpenSearch as a vector database.
    Example:
        .. code-block:: python
            from langchain import OpenSearchVectorSearch
            opensearch_vector_search = OpenSearchVectorSearch(
                "http://localhost:9200",
                "embeddings",
                embedding_function
            )
    """
    def __init__(
        self, opensearch_url: str, index_name: str, embedding_function: Embeddings
    ):
        """Initialize with necessary components."""
        self.embedding_function = embedding_function
        self.index_name = index_name
        self.client = _get_opensearch_client(opensearch_url)
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        bulk_size: int = 500,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            bulk_size: Bulk API request count; Default: 500
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        embeddings = [
            self.embedding_function.embed_documents(list(text))[0] for text in texts
        ]